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Standard Template Library STL

Standard Template Library STL. What are templates and STL and how to use them? Some common data-structures Comparator functions Some more datastructures Iterators Algorithms (sort, find, reverse, ...) ‏ Other templated types (pair, complex, string and rope) ‏

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Standard Template Library STL

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  1. Standard Template LibrarySTL • What are templates and STL and how to use them? • Some common data-structures • Comparator functions • Some more datastructures • Iterators • Algorithms (sort, find, reverse, ...)‏ • Other templated types (pair, complex, string and rope)‏ • Efficiencies of the common Data-Structures • How to use the STL docs

  2. In the beginning.. • Say I want to create a queue of integers • Good fine, after a bit of work this can be done • Now I want a queue of strings... • ok... remake the queue but with strings this time • Wait? Haven't I just done 2 times the work? • Yes... yes I have • Wouldn't it be nice if you could just do it once... • Yes... yes it would :)‏

  3. Introducing Templates • Templates are a way of creating a datastructure once so that it can assigned any arbitrary datatype after • Similar to generic types in Java (but NOT the same)‏ • E.g. We create a template queue once • Now we can easily use a string version and an integer version without having to recode it.

  4. Introducing The STL • Turns out you won't actually have to code any template classes yourself anyway • It's all been done for you • The Standard Template Library: • A library of standard templates • vectors, queues, priority_queues, sets, maps ... etc etc etc • Very powerful • Very fast • Very flexible

  5. Templates in C++: Syntax • STL has the vector<T> templated class • If we want a vector of ints we simply use: • vector<int> my_integers; • Or for doubles • vector<double> my_doubles; • Or for any class you want • class my_node_class {int n; double weight; ...}; • vector<my_node_class> my_nodes;

  6. Why STL is fantastic! • Fast – optimised to hell and back! • All templates are made at compile time • It's as if someone quickly codes up the specific data-structure for you just before compiling • Unlike Java (which does it at run-time ... very very slow)‏ • Powerful and Vast • Easy to make any datastructure of any type • Can also make arrays of templated types • vector<int> [N]; (you can't do this in Java!)‏ • There are more then enough datastructures to suit any situation • And it's all done for you!!!!

  7. Common Data-Structures • Vector • List • Queue • Stack • Map • Priority Queue • Set • Hashes • ...

  8. Sequences • List - #include <list> • Your standard issue linked list (doubly linked)‏ • list<double> my_list; • my_list.push_back(42); // adds to back of array • my_list.push_front(21); // adds to front of array • double d = my_list.back(); // gets item at back of list • double d2 = my_list.front(); // gets item at front of list • my_list.pop_back(); // removes back item from list • my_list.pop_front(); // removes front item from list • Can also insert in the middle (explained a bit later)‏

  9. Sequences • Vector - #include <vector> • Resizeable array • vector<int> my_vec; // array size 0 • vector<int> my_vec(100); // array size 100 • Has same operations as list • push_back(), push_front(), front(), back() ... • Can use standard [] notation (operator overloading!)‏ • my_vec[3] = 11; • int my_int = my_vec[9];

  10. Queues and Stacks • Queue - #include <queue> • queue<double> Q; // empty queue • Q.push_back(3.14); • Q.push_back(2.7)‏ • double d = Q.top(); // will be 3.14 • Q.pop(); // removes the top element from the queue • Stack - #include <stack> • Works in the same way as queue, except FIFO

  11. Sorted data-structures • These datastructures require some form or order • Either have to give it a less-then function or define the less-then operator (<)‏ • operator< already defined for int, double, float etc • Can define it for whatever class you want class my_class { int a, b; double c; bool operator<(const my_class& m) const { return c < m.c;} }; • Can also define the == operator similarly

  12. Maps • Map - #include <map> • Maps one type to another - map<key, data> • map<int, string> my_map; • my_map[1] = ”One”; • String s = my_map[1]; // will be ”One” • String s2 = my_map[3]; // will be default value of ”” • Can map anything to anything • map<string, int> string_map; • string_map[”January”] = 31;

  13. Priority Queue • Priority Queue - #include <queue> • Must have operator< defined • priority_queue<int> P; • Same commands as a normal queue • P.push(), P.top(), P.pop()‏ • Except top will return the 'largest' value • Depending on how you define large • If you want the smallest value • Define large to be small ;)‏ • return return c > m.c;

  14. General functions • By now you would have seen that some functions are common to nearly all structures • .size() returns the number of elements • .empty() returns whether there are elements at all • Rather use .empty() instead of .size() == 0 • Since .size() might not be O(1) - can anyone say list? • You've already seen front(), back() push_back() etc... • These are common to most structures (not all)‏ • Check the docs if you are unsure

  15. Iterators • Having a structure is great • But what if you want to go through all the elements of a structure? • Use iterators! • Almost all STL data-structures have iterators • Like priority_queues don't have iterators

  16. Iterators: Example vector<my_class> my_vec; ... // adding stuff to my_vec for (vector<my_class>::iterator i = my_vec.begin() ; i != my_vec.end() ; i++)‏ { // note! It is *i, not i (the astrik dereferences the iterator)‏ cout << *i << endl; cout << (*i).a << endl; cout << i->a << endl; // -> just a shorthand way of writing (*i). } • Can do this with list, set, queue, stack... • Check documentation for more info

  17. Whats going on here!? • vector<my_class>::iterator i = my_vec.begin()‏ • Like int i = 0; • i++ • This is like i.iterate() or i.next(). Just move on to the next element • i != my_vec.end()‏ • my_vec.end() points to the position just after the last element in the datastructure

  18. Iterators: my_vec.end(); • Why do we say != instead of < ?? • There is no sense of less then in an iterator. • Say we are at the last element of the list: • i++ will then make i point to the position just after the list • the position just after the list == my_vec.end()‏ • Also useful as a 'NULL' value (c.f. algorithms...)‏

  19. Other Iterator stuff • Some iterators are bidirectional (i.e. can use i--)‏ • Reverse iterators • Backward traversal of a list • For( list<int>::reverse_iterator i = my_list.r_begin() ; i != my_list.r_end(); i–)‏ • For vectors: • [] operator is slightly slower then useing iterators

  20. Now that you know iterators... • list<int>::iterator i; // and i is in the middle of the list • my_list.insert(i, 45); // inserts 45 just before i • Same for vectors • my_list.erase(i); // erases element at i • But what if you have this for (list<int>::iterator i = my_list.begin(); i != my_list.end() ; i++) { if (*i == 45)‏ my_list.erase(i); } insert(iterator pos, const T& x)‏

  21. Erasing elements for (list<int>::iterator i = my_list.begin(); i != my_list.end() ; i++) { if (*i == 45)‏ my_list.erase(i); } • The item at i will be erased • When the next loop comes around, i++ will be called • But we just deleted i ! for (list<int>::iterator i = my_list.begin(); i != my_list.end() ; i++) { if (*i == 45)‏ my_list.erase(i--); // problem solved }

  22. Sets • Set - #include<set> • Unique Sorted set of elements • So no two elements will be the same • Must have operator< defined • Since iterator will run through them in order • set<double> my_set; • my_set.insert(3.1459); • my_set.remove(11.5);

  23. Set iterators • upper and lower bounds of a set • set<point>:iterator = my_set.lower_bound(10); • Returns the first element that is >= 10 • set<point>:iterator = my_set.upper_bound(90); • Returns the first element that is <= 90 • So a set {1, 4, 15, 39, 89, 90, 102, 148} • my_set.lower_bound(10); //will point to 4 • my_set.upper_bound(90); //will point to 90 lower_bound

  24. Hash Sets • Hash Set - #include <ext/hash_set> • using namespace __gnu_cxx; • hash_set<const char *> my_hash_set; • my_hash_set.insert(”a string”); • my_hash_set.insert(”another string”); • my_hash_set.find(”a string”); // returns an iterator • Returns my_hash_set.end() if not found

  25. Hash Map • Hash Map - #include <ext/hash_map> • using namespace __gnu_cxx; • Like a map • hash_map<int, const char *> my_hash_map; • my_hash_map[3] = ”a string”;

  26. The Hashing Function • As you know the hash set and hash map need a hashing function • This is already defined for int, double, float, char byte, short, long and const char * • If you use your own class then you have to provide your own hashing function • Use function objects (explained later)‏ • hash_set<my_class, my_hash_func> my_hash_set;

  27. Algorithms • We have this lovely general way of using data-structures: • Why don't we use them to write general algorithms? • We do! (by ”we” I mean the people who wrote STL)‏ • sort(), find(), unique(), count(), reverse() are all general algorithms at your disposal • There are others... • #include <algorithm>

  28. Algorithms: Types • Algorithms can loosely be group into 2 categories • Data Transformation: These algorithms transform your data by applying operations on them. Can overwrite the original or copy to a new container. eg: reversing, sorting, etc • Data Information: These algorithms retrieve information about your data. eg: minimum element, searching, etc

  29. Algorithms: Before we begin • A lot of algorithms use function objects. • Function objects are just objects of classes that have the ( ) operator overloaded. • Function objects must have the correct parameters for your program to compile. • Can often be interchangable with functions themselves.

  30. Algorithms: Before we begin • This is legal • vector<double> my_vec; • sort(my_vec.begin(), my_vec.end()); • And so is this • double my_arr[N]; • sort(my_arr, my_arr+N);

  31. Algorithms: Transformations • copy(myArr, myArr+N, myVec.begin()); • copy_n(myArr, N, myVec.begin()); • copy_backward(myArr, myArr+N, myVec.end()); • Copies data from one place in memory to another. • Can specify iterators for the range to copy or specify a iterator to the beginning of a range. • Usually copies from start to end, but can do the other way.

  32. Algorithms: Transformations • swap(a, b); • Swaps two values. • iter_swap(myArr+3, myArr+4); • Swaps two values of iterators. • swap_ranges(myArr+1, myArr+N/2, myArr+1+N/2); • Swaps two ranges specified by the beginning and end of the first range and the beginning of the second.

  33. Algorithms: Transformations • transform(myArr, myArr+N, myVec.begin(), fabs)‏ • Transforms all the elements in the range specified by the first two iterators and stores the result in the third iterator. The last parameter is a unary function object or function giving the result of the transformation. • transform(myArr, myArr+N, myVec.begin(), myVec.begin(), pow)‏ • Same as above, except with a binary function. Need to specify an extra iterator to the beginning of a second range.

  34. Algorithms: Transformations • fill(myArr, myArr+N. setValue); • Sets all values in the range of the first two iterators to the set value. • fill_n(myArr, N, setValue); • Same as above, but can specify exactly how many elements to fill. • generate(myArr, myArr+N, functionObject); • generate_n(myArr, N, functionObject); • Same as the above, but can specify a function object that takes no arguments to get a value to fill each element.

  35. Algorithms: Transformations • unique(myArr, myArr+N); • Removes consecutive duplicate items specified by the range. • unique(myArr, myArr+N, binaryPredicate); • Removes consecutive duplicate items specified by the range, and using the binary predicate to test for equality. • Does NOT remove all duplicates in a range, however, if the range is sorted, all duplicates in that range will be removed. • Also copy versions.

  36. Algorithms: Transformations • reverse(myArr, myArr+N); • Reverses the range specified by the iterator. • Also a copy version to store the reversed range in a new container.

  37. Algorithms: Transformations • sort(myArr, myArr+N); • Sorts the range specified. • Uses the < operator to compare elements. • Guaranteed O(Nlog(N)). Uses a introsort. • stable_sort(myArr, myArr+N); • Same as above, but is stable. • Separate sort functions for linked lists.

  38. Algorithms: Transformations • A few others functions for transforming data. • Statistical functions for finding random samples and shuffling the data. • Mathematical functions for finding unions, intersections, etc of sets. • Functions for finding permutations of your set. • Functions for find the n-th 'smallest' element in your set.

  39. Algorithms: Information • find(myArr, myArr+N, findValue); • Performs a linear search on the range. Returns the first iterator such that the value at that iterator is equal to findValue. • find_if(myArr, myArr+N, predicate); • Same as above, but instead of testing for equality with a specific element, it tests for truth of a predicate. • Also find_first_of which searches for the first of a list of values in the range.

  40. Algorithms: Information • lower_bound(myArr, myArr+N, findValue); • Performs a binary search to return an iterator to the first appearance of findValue in the range. • upper_bound(myArr, myArr+N, findValue); • Same as above, but returns an iterator to 'one past' the last element equal to findValue. • equal_range(myArr, myArr+N, findValue); • Same as above, but returns a pair of iterators representing the range on which all values equal findValue.

  41. Algorithms: Information • binary_search(myArr, myArr+N, findValue)‏ • Returns true if the findValue is in the range specified by the iterators and false otherwise. • All four of the binary search functions can also take comparators. • Reminder: Comparators are binary predicates, ie: function objects which take two objects and return a boolean value.

  42. Algorithms: Information • Several other functions that can be used to get information. • Mathematical functions that allow you to calculate the minimum and maximum of sets, sum of elements, etc.

  43. Other templated types • pair<T, Y> • basically two objects lumped together • e.g. pair<int, double> could represent an index and an associated weight • can have a pair<double, pair<int,int> > • represents a weight and two nodes (perhaps...)‏ • pair<double, pair<int,int>>; WRONG!!!! • c++ gets confused with the >> operator (just use a space)‏ • Comparisons compare first, then second.

  44. Accessing pairs • to access elements in the pair: • pair <string, int> my_pair; • my_pair.first = ”a string”; • my_pair.second = 5; • my_pair = make_pair(”another string”, 42); • Can have arrays of pairs • pair <int, int> edges [N]; • edges[5].first = 64;

  45. Complex numbers • complex - #include<complex> • Can be treated like a pair of numbers (x,y), • but with certain mathematical functions that are quite useful • complex<double> coord; // vector • Typically complex<T> can be treated as a handy built-in 2D vector class. • A = a + bi, conj(A) = a – bi • real(A) = a, imag(A) = b • conj(A)xB = A.B + (AxB)i

  46. String and Rope • STL provides two data structures for character strings. • string • Your normal familiar string. • Provides functions like substring, length, etc. • Provides functions for getting the underlying string data. • rope • Not your normal familiar string. • Better than strings in certain circumstances, however more complicated and unnecessary. Different semantics to string.

  47. Efficiencies • unsorted array

  48. Efficiencies • sorted array

  49. Efficiencies • list

  50. Efficiencies • vector

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